During the first cycle of the program project on "Molecular Biomarkers for Environmental Carcinogens", we have established close links with the projects that have led to several publications including the application of regression models for the specific research questions. Furthermore, we have developed new methodologies for the incorporation of different sources of variability for the establishment and use of calibration curves. As part of the renewal of the program project, we will continue to collaborate with investigators of the three projects in the design and analysis of data, and in the development of methods relevant to the issues of the research projects. The design, conduct and analysis of the experiments and studies to be conducted by the components of the program project require expertise in biostatistics and epidemiology. The specific aims of the Biostatistics/Epidemiology Core are: 1) To establish methods that promote adherence to standard protocols with particular attention to data collection and management. The Biostatistics/Epidemiology Core will monitor the data entry; will implement data editing procedures; will design a data base to expedite analysis and linkage of the different components of data within and between projects; and will establish systems for data security and back- up. 2) To collaborate with investigators in the projects of the program for the purpose of study design and data analysis. The Core will contribute with expertise in epidemiological principles of study design to obtain valid (i.e. non-biased) inferences and a level of data collection which will allow for the estimation of the simultaneous effects of different factors (e.g., exposure to aflatoxin, HBV status and demographic characteristics). Data from the projects will require the implementation of multivariate regression methods for analyses of longitudinal data and for binary and time to failure outcomes. Furthermore, we will use methods for the evaluation of treatments on clinical trials using changes of biomarkers as the primary outcomes measure. 3) To develop new statistical methods appropriate for the data generated by the projects which will allow for testing.